Research On Indoor Robot Path Planning Based On IFC Spatial Data And Improved Genetic Algorithm

In recent years,automation and robotics technology have brought great changes to human society.In particular,indoor robots,as assistants in our daily life,play an increasingly important role.Large public buildings with complex interior space require more mature robot path planning technology.The key of indoor robot to complete indoor work is to establish a comprehensive and accurate indoor space expression model,and on this basis to carry out efficient and accurate path planning.This paper presents an automatic path planning method and the determination of the optimal path for the two-dimensional interior space of large public buildings based on IFC digital model.Through comparison and analysis of existing interior space representation models,the BIM model based on IFC provides rich geometric and semantic information about the physical space components of the building.The paper selects the semantic,geometric,topological features of the IFC model and grid method to generate the navigation map,while considering the static obstacles in the indoor space environment and the accessibility state of space and transition.This paper uses the search method based on obstacles.First,it searches the optimal path by finding the key obstacles and effectively bypassing them.The key obstacles affecting the indoor path planning are mainly determined by considering the moving direction of the robot,and the effective free element is selected based on the key obstacles.The method can effectively reduce the scope of the search space,so that the indoor robot can determine a better path than the random selection method.This paper proposes a fitness function to optimize the path complexity cost of the robot by reducing the number of turns in the path.An improved genetic algorithm using the same adjacency crossover operator is proposed to solve the problem of indoor robot path planning better.Through an actual case,the paper compares the experimental results of the improved genetic algorithm before and after the improvement,and verifies the usability and accuracy of the indoor robot path planning method based on the same adjacency crossover-operator genetic algorithm.